AIMC Topic: Artifacts

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Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI.

European journal of radiology Feb 12, 2025
PURPOSE: This study aimed to assess the image quality and the diagnostic value of deep learning reconstruction (DLR) for diffusion-weighted imaging (DWI) compared with conventional single-shot echo-planar imaging (ss-EPI) in 3 T breast MRI.
Artifacts Diffusion Magnetic Resonance Imaging Echo-Planar Imaging Humans Breast Prospective Studies Image Interpretation, Computer-Assisted Female Aged Adult Breast Neoplasms Middle Aged Deep Learning
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Does Deep Learning Reconstruction Improve Ureteral Stone Detection and Subjective Image Quality in the CT Images of Patients with Metal Hardware?

Journal of endourology Feb 11, 2025
Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and...
Humans Tomography, X-Ray Computed Deep Learning Metals Ureteral Calculi Image Processing, Computer-Assisted Artifacts Radiation Dosage Hip Prosthesis
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Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework.

IEEE journal of biomedical and health informatics Feb 10, 2025
High-frequency oscillations (HFOs) in intracranial EEG (iEEG) recordings are critical biomarkers for localizing the seizure onset zone (SOZ) in patients with focal refractory epilepsy. Despite their clinical significance, HFO analysis is often compro...
Electrocorticography Young Adult Algorithms Adult Male Brain Humans Artifacts Electroencephalography Female Machine Learning Epilepsies, Partial Signal Processing, Computer-Assisted
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DeepReducer: A linear transformer-based model for MEG denoising.

NeuroImage Feb 8, 2025
Measuring event-related magnetic fields (ERFs) in magnetoencephalography (MEG) is crucial for investigating perceptual and cognitive information processing in both neuroscience research and clinical practice. However, the magnitude of the ERF in cort...
Signal-To-Noise Ratio Humans Deep Learning Magnetoencephalography Signal Processing, Computer-Assisted Brain Artifacts
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Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction.

Medical image analysis Feb 7, 2025
Magnetic resonance fingerprinting (MRF) is a promising technique for fast quantitative imaging of multiple tissue parameters. However, the highly undersampled schemes utilized in MRF typically lead to noticeable aliasing artifacts in reconstructed im...
Artifacts Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Algorithms Brain Humans
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Deep learning aided determination of the optimal number of detectors for photoacoustic tomography.

Biomedical physics & engineering express Feb 7, 2025
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology...
Tomography Algorithms Deep Learning Image Processing, Computer-Assisted Artifacts Phantoms, Imaging Photoacoustic Techniques Humans Neural Networks, Computer
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A dual-domain network with division residual connection and feature fusion for CBCT scatter correction.

Physics in medicine and biology Feb 7, 2025
This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in cone-beam computed tomography (CBCT).The proposed network comprises a projection-domain sub-network and an image-domain sub...
Cone-Beam Computed Tomography Image Processing, Computer-Assisted Artifacts Neural Networks, Computer Scattering, Radiation
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Syn2Real: synthesis of CT image ring artifacts for deep learning-based correction.

Physics in medicine and biology Feb 5, 2025
. We strive to overcome the challenges posed by ring artifacts in x-ray computed tomography (CT) by developing a novel approach for generating training data for deep learning-based methods. Training such networks require large, high quality, datasets...
Deep Learning Image Processing, Computer-Assisted Artifacts Humans Tomography, X-Ray Computed
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Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach.

BMC medical imaging Feb 4, 2025
BACKGROUND: Computed tomography attenuation correction (CTAC) is commonly used in cardiac SPECT imaging to reduce soft-tissue attenuation artifacts. However, CTAC is prone to inaccuracies due to CT artifacts and SPECT-CT mismatch, along with addition...
Image Processing, Computer-Assisted Heart Artifacts Middle Aged Deep Learning Tomography, Emission-Computed, Single-Photon Humans Tomography, X-Ray Computed Female Aged Male
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FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Journal of neural engineering Feb 4, 2025
Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel metho...
Deep Learning Electroencephalography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Signal-To-Noise Ratio Attention Artifacts
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